PdVGG    PdVGG, developed by INFN Padova, addresses the event reconstruction with a single-task approach, i.e. training one network for particle classification, one for energy and one for direction reconstruction.
Since state-of-the-art API for CNNs take rank 3 tensors as input (rectangular images, square pixels), and the LST camera is hexagonal composed of hexagonal pixels, images need to be appropriately preprocessed in order to feed the networks.
To perform this step, images were fitted using bicubic interpolation and then resampled onto a rectangular lattice.
A handcrafted network of 13 layers based on the Visual Geometry Group architecture (VGG) [1]} has been implemented. VGG is a solid, popular architecture, often used as a baseline model in the deep learning community.
Among the different techniques used, the Cyclic Learning Rate technique [2]} allows the network to explore different minima of the so-called loss functionA function that, during the training of a CNN, measures how much the predicted values deviate from the true, a-priori known values. Thus, the goal of the algorithm is to find the best parameters of the network that minimize this function., obtaining different network configurations in one single training.
